Identification of noisy AR systems using damped sinusoidal model of autocorrelation function

Author

Hasan, Md Kamrul ; Fattah, S. Anowarul ; Khan, M. Rezwan

Author_Institution

Dept. of Electr. & Electron. Eng., Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh

Volume

10

Issue

6

fYear

2003

fDate

6/1/2003 12:00:00 AM

Firstpage

157

Lastpage

160

Abstract

This letter presents a novel method for minimum-phase autoregressive (AR) system identification at a very low SNR using damped sinusoidal model representation of the autocorrelation function of the noise-free AR signal with guaranteed stability. The new model parameters are estimated solely from the given noisy observations. Then AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters. The simulation results show that the proposed method can estimate the AR system parameters with high accuracy even at an SNR as low as -5dB.

Keywords

autoregressive processes; correlation theory; noise; parameter estimation; signal representation; autocorrelation function; damped sinusoidal model; minimum phase autoregressive system identification; noise-free AR signal; noisy AR systems; noisy observations; representation; Additive noise; Autocorrelation; Computational complexity; Equations; Low-frequency noise; Parameter estimation; Signal processing; Signal to noise ratio; Stability; White noise;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2003.811590

Filename

1198663